AI in Production: The 2026 Benchmark Report - Inngest Blog

The 2026 Benchmark Report examines the challenges and infrastructure choices faced by backend, full-stack, and AI engineers in maintaining reliable AI workflows in production. It highlights a significant confidence gap, with only 19% of teams feeling assured in their infrastructure's scalability, especially in larger organizations. The report identifies three main issues: the growing reliability burden due to AI use cases, unresolved observability problems, and the necessity of using observable, composable stacks for confidence. Teams spending considerable time on reliability work often lack the tools for effective observability, which is crucial for diagnosing failures quickly. The report suggests that durable execution tools, particularly those integrating orchestration with observability, offer better reliability outcomes. It also notes that AI frameworks and evaluation methods are underdeveloped, with many teams either not using them or building their own solutions. Ultimately, the report calls for integrated, context-rich tooling that provides a clear view of workflow execution and failures, aiming to improve reliability and scalability in AI-driven environments.

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